Shu Fai Cheung

This personal website hosts the files and related links of the selected publications by my collaborators and me.

– Shu Fai Cheung

Methodological work

Tools

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• Pesigan, I. J. A., Cheung, S. F., Wu, H., Chang, F., & Leung, S. O. (2026). How plausible is my model? Assessing model plausibility of structural equation models using Bayesian posterior probabilities (BPP). Behavior Research Methods, 58(3), Article 73. https://doi.org/10.3758/s13428-025-02921-x
  [ Open Access: Full text from the publisher ]
  
  • The R package modelbpp (View on CRAN)
  • Files at OSF project page

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• Cheung, S. F., & Lai, M. H. C. (2026). semfindr: An R package for identifying influential cases in structural equation modeling. Multivariate Behavior Research. Advance online publication. https://doi.org/10.1080/00273171.2026.2634293
  [ Limited eprint from the publisher ]
  
  • The R package semfindr (View on CRAN)
  • Files at OSF project page

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• Yang, W., & Cheung, S. F. (2026). Forming bootstrap confidence intervals and examining bootstrap distributions of standardized coefficients in structural equation modelling: A simplified workflow using the R package semboottools. Behavior Research Methods, 58(2), Article 38. https://doi.org/10.3758/s13428-025-02911-z
  [ ShareIt link from the publisher ]
  
  • The R package semboottools (View on CRAN)
  • Files at OSF project page

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• Cheung, S. F., & Cheung, S.-H. (2024). manymome: An R package for computing the indirect effects, conditional effects, and conditional indirect effects, standardized or unstandardized, and their bootstrap confidence intervals, in many (though not all) models. Behavior Research Methods, 56(5), 4862-4882. https://doi.org/10.3758/s13428-023-02224-z
  [ Open Access: Full text from the publisher ]
  
  • The R package manymome (View on CRAN)
  • Files at OSF project page

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• Cheung, S. F., & Pesigan, I. J. A. (2023). semlbci: An R package for forming likelihood-based confidence intervals for parameter estimates, correlations, indirect effects, and other derived parameters. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 985–999. https://doi.org/10.1080/10705511.2023.2183860
  [ Limited eprint from the publisher ]
  [ PDF File of accepted version ]
  • The R package semlbci (View on CRAN)
  • Files at OSF project page

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• Pesigan, I. J. A., Sun, R. W., & Cheung, S. F. (2023). betaDelta and betaSandwich: Confidence intervals for standardized regression coefficients in R. Multivariate Behavioral Research, 58(6), 1183–1186. https://doi.org/10.1080/00273171.2023.2201277
  [ Limited free eprints from the publisher ]

  • R package betaDelta (View on CRAN)
  • R package betaSandwich (View on CRAN)

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• Cheung, S. F., Pesigan, I. J. A., & Vong, W. N. (2023). DIY bootstrapping: Getting the nonparametric bootstrap confidence interval in SPSS for any statistics or function of statistics (when this bootstrapping is appropriate). Behavior Research Methods, 55(2), 474–490. https://doi.org/10.3758/s13428-022-01808-5
  [ Access PDF ]

  • Latest versions at GitHub
  • Files at OSF project page

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• Cheung, S. F., & Pesigan, I. J. A. (2023). FINDOUT: Using either SPSS commands or graphical user interface to identify influential cases in structural equation modeling in AMOS. Multivariate Behavioral Research, 58(5), 964–968. https://doi.org/10.1080/00273171.2022.2148089
  [ Limited free eprints from the publisher ]
  [ PDF File of accepted version ]

  • Files at OSF Project page

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• Cheung, S. F., Cheung, S.-H., Lau, E. Y. Y., Hui, C. H., & Vong, W. N. (2022). Improving an old way to measure moderation effect in standardized units. Health Psychology, 41(7), 502–505. https://doi.org/10.1037/hea0001188
  [ PDF File of accepted version ]

  • R package stdmod (View on CRAN)
  • Files at OSF project page

Simulation studies and theoretical work

• Cheung, S. F., & Aguiar, M. (2025). Doing, reporting, and interpreting moderation right: Recommendations for selected common practices. Journal of Pacific Rim Psychology, 19. https://doi.org/10.1177/18344909251374823 [ Open Access: Full text from the publisher ]

  • Files at OSF project page

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• Wu, H., Cheung, S. F., & Leung, S. O. (2020). Simple use of BIC to assess model selection uncertainty: An illustration using mediation and moderation models. Multivariate Behavioral Research, 55(1), 1–16. https://doi.org/10.1080/00273171.2019.1574546
  [ Limited free eprints from the publisher ]

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• Pesigan, I. J. A., & Cheung, S. F. (2020). SEM-based methods to form confidence intervals for indirect effect: Still applicable given nonnormality, under certain conditions. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.571928
  [ Fulltext ]

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• Sun, R. W., & Cheung, S. F. (2020). The influence of nonnormality from primary studies on the standardized mean difference in meta-analysis. Behavior Research Methods, 52(4), 1552–1567. https://doi.org/10.3758/s13428-019-01334-x
  [ Access PDF ]

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• Cheung, S. F., Sun, R. W., & Chan, D. K.-S. (2019). Correlation-based meta-analytic structural equation modeling: Effects of parameter covariance on point and interval estimates. Organizational Research Methods, 22(4), 892–916. https://doi.org/10.1177/1094428118770736
  [ PDF File of accepted version ]

  • Files at OSF project page

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• Cheung, M. W.-L., & Cheung, S. F. (2016). Random-effects models for meta-analytic structural equation modeling: Review, issues, and illustrations. Research Synthesis Methods, 7, 140–155. https://doi.org/10.1002/jrsm.1166

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• Cheung, S. F., Chan, D. K.-S., & Sun, R. W. (2019). Meta-analyzing dependent correlations with correction for artifacts that multiplicatively attenuate the true correlation. Behavior Research Methods, 51(2), 793–810. https://doi.org/10.3758/s13428-018-1111-y
  [ Access PDF ]

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• Cheung, S. F., & Chan, D. K.-S. (2014). Meta-analyzing dependent correlations: An SPSS macro and an R script. Behavior Research Methods, 46(2), 331–345. https://doi.org/10.3758/s13428-013-0386-2
  [ Access PDF ]

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• Cheung, S. F., & Chan, D. K.-S. (2008). Dependent correlations in meta-analysis: The case of heterogeneous dependence. Educational and Psychological Measurement, 68(5), 760–777. https://doi.org/10.1177/0013164408315263
  [ PDF File of accepted version ]

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• Cheung, S. F., & Chan, D. K.-S. (2004). Dependent effect sizes in meta-analysis: Incorporating the degree of interdependence. The Journal of Applied Psychology, 89(5), 780–791. https://doi.org/10.1037/0021-9010.89.5.780
  [ PDF File of accepted version ]